Recently, deep learning-based methods have been applied to medical image registration and motion tracking. They are fast and have achieved at least comparable accuracy with conventional registration methods. Among those approaches, supervised methods [1]} require ground truth deformation fields, which are usually synthetic. Registration accuracy thus will be limited by the quality of synthetic ground truth. Unsupervised methods [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} learn the deformation field by a loss function of the similarity between the fixed image and warped moving image. Unsupervised methods have been extended to cover deformable and diffeomorphic models. Deformable models [8]}, [2]}, [3]} aim to learn the single directional deformation field from the fixed image to the moving image. Diffeomorphic models [9]}, [5]}, [13]}, [12]} learn the stationary velocity field (SVF) and integrate the SVF by a scaling and squaring layer, to get the diffeomorphic deformation field [9]}. A deformation field with diffeomorphism is differentiable and invertible, which ensures one-to-one mapping and preserves topology. Inspired by these works, we propose to use a bi-directional diffeomorphic registration network to track motions on t-MRI images.
